Course Outline

ICT706 Machine Learning

Course Coordinator:Andrew Lang (alang1@usc.edu.au) School:School of Science, Technology and Engineering

2026Trimester 1

UniSC Sunshine Coast

UniSC Adelaide

Blended learning Most of your course is on campus but you may be able to do some components of this course online.

Online

Online You can do this course without coming onto campus, unless your program has specified a mandatory onsite requirement.

Please go to unisc.edu.au for up to date information on the
teaching sessions and campuses where this course is usually offered.

What is this course about?

Description

Massive amounts of data are collected in almost every corner of the world, and they become the new strategic mechanisms for intelligent businesses. This course covers both foundational knowledge and more advanced practical skills about data processing and analysis. It explores the use of, and techniques used in, exploratory, descriptive, and predictive analytics. Combining technical and statistical skills, analytical thinking, and business acumen, it helps you to harness the power of data analytics.

How will this course be delivered?

Activity Hours Beginning Week Frequency
Blended learning
Learning materials – Asynchronous Learning material 2hrs Week 1 12 times
Tutorial/Workshop 1 – Synchronous on campus workshop 2hrs Week 1 12 times
Seminar – On campus seminar 1hr Week 1 2 times
Online
Learning materials – Asynchronous Learning material 2hrs Week 1 12 times
Tutorial/Workshop 1 – Synchronous Zoom workshop 2hrs Week 1 12 times
Seminar – Online seminar 1hr Week 1 2 times

Course Topics

Introduction to data analytics and data science

Data quality issues and pre-processing

Exploratory data analysis and visualisation

Data relationships: association rules and clustering

Machine learning: linear regression, decision trees, deep learning, artificial neural networks

 

What level is this course?

700 Level (Specialised)

Demonstrating a specialised body of knowledge and set of skills for professional practice or further learning. Advanced application of knowledge and skills in unfamiliar contexts.

What is the unit value of this course?

12 units

How does this course contribute to my learning?

Course Learning Outcomes On successful completion of this course, you should be able to... Graduate Qualities Completing these tasks successfully will contribute to you becoming...
1 Demonstrate a specialised and integrated understanding of contemporary data science and business analytics theories and practices. Knowledgeable
Empowered
2 Use data mining, machine learning and data analysis techniques to develop relevant and rigorous models to gain business insights. Knowledgeable
Creative and critical thinker
3 Investigate, evaluate, and plan the lifecycle of data through an organisation. Knowledgeable
4 Apply computer technology in the solution of business analytics problems. Creative and critical thinker
Engaged

Am I eligible to enrol in this course?

Refer to the UniSC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.

Pre-requisites

ICT703

Co-requisites

Not applicable

Anti-requisites

Not applicable

Specific assumed prior knowledge and skills (where applicable)

Not applicable

Microcredential Information

Not applicable

How am I going to be assessed?

Grading Scale

Standard Grading (GRD)

High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL).

Details of early feedback on progress

Feedback will be provided for the formative exercises in the weekly computer workshops. This feedback will give students immediate feedback on their understanding and progress in the course.

Assessment tasks

Delivery mode Task No. Assessment Product Individual or Group Weighting % What is the duration / length? When should I submit? Where should I submit it?
All 1 Examination - not Centrally Scheduled Individual 10%
1 hour
Week 6 Online Test (Quiz)
All 2 Artefact - Technical and Scientific, and Written Piece Individual 40%
2,000 words
Week 11 Online Assignment Submission with plagiarism check
All 3 Examination - not Centrally Scheduled Individual 50%
2 hours
Exam Period Online Test (Quiz)
All - Assessment Task 1:Data analytics test
Goal:
To learn about the concepts of machine learning using hands on tools. This task enables you to apply computer tools to solve business problems
Product: Examination - not Centrally Scheduled
Authorship Statement:
Format:
Individual online exam. Further details will be available on Canvas.
Criteria:
No. Learning Outcome assessed
1
Selection, adaption and design of solutions using machine learning techniques
4
Generic Skills:
All - Assessment Task 2:Research project
Goal:
To undertake a data analytics approach to solve a set of business problems that require the use of appropriately selected data processing and mining approaches.
Product: Artefact - Technical and Scientific, and Written Piece
Authorship Statement:
Format:
This is an individual assessment. The assessment will report the set of business problems, data required, and data mining tools selected to solve the selected problems. Further details will be available on Canvas.
Criteria:
No. Learning Outcome assessed
1
Development of data processing and mining solutions to solve a business problems
4
2
Analysis of data analysis methods used in an organisation
2
3
Clear summary of relevant information and outcomes
1
Generic Skills:
All - Assessment Task 3:Final Examination
Goal:
This assessment task will demonstrate your knowledge and application of all material covered in this course.
Product: Examination - not Centrally Scheduled
Authorship Statement:
Format:
A final examination will be held in the examination period. This is an individual assessment.
Criteria:
No. Learning Outcome assessed
1
Demonstration of skills and knowledge in the data analytics and machine learning
1 3
Generic Skills:

Directed study hours

A 12-unit course will have total of 150 learning hours which will include directed study hours (including online if required), self-directed learning and completion of assessable tasks. Student workload is calculated at 12.5 learning hours per one unit.

What resources do I need to undertake this course?

Please note: Course information, including specific information of recommended readings, learning activities, resources, weekly readings, etc. are available on the course Canvas site– Please log in as soon as possible.

Prescribed text(s) or course reader

You need regular access to the resource(s) below. Many texts are available as ebooks through the Library at no additional cost.

Required? Author Year Title Edition Publisher
Required Foster Provost,Tom Fawcett 2013 Data Science for Business n/a Oreilly & Associates Incorporated

Specific requirements

Not applicable

How are risks managed in this course?

Health and safety risks for this course have been assessed as low. It is your responsibility to review course material, search online, discuss with lecturers and peers and understand the health and safety risks associated with your specific course of study and to familiarise yourself with the University’s general health and safety principles by reviewing the online induction training for students, and following the instructions of the University staff.

What administrative information is relevant to this course?

Assessment: Academic Integrity

Academic integrity is the ethical standard of university participation. It ensures that students graduate as a result of proving they are competent in their discipline. This is integral in maintaining the value of academic qualifications. Each industry has expectations and standards of the skills and knowledge within that discipline and these are reflected in assessment.

Academic integrity means that you do not engage in any activity that is considered to be academic fraud; including plagiarism, collusion or outsourcing any part of any assessment item to any other person. You are expected to be honest and ethical by completing all work yourself and indicating in your work which ideas and information were developed by you and which were taken from others. You cannot provide your assessment work to others. You are also expected to provide evidence of wide and critical reading, usually by using appropriate academic references.

In order to minimise incidents of academic fraud, this course may require that some of its assessment tasks, when submitted to Canvas, are electronically checked through Turnitin. This software allows for text comparisons to be made between your submitted assessment item and all other work to which Turnitin has access.

Assessment: Additional Requirements

Eligibility for Supplementary Assessment

Your eligibility for supplementary assessment in a course is dependent of the following conditions applying:
(a) The final mark is in the percentage range 47% to 49.4%; and
(b) The course is graded using the Standard Grading scale

Assessment: Submission penalties

Late submissions may be penalised up to and including the following maximum percentage of the assessment task’s identified value, with weekdays and weekends included in the calculation of days late:
(a) One day: deduct 5%;
(b) Two days: deduct 10%;
(c) Three days: deduct 20%;
(d) Four days: deduct 40%;
(e) Five days: deduct 60%;
(f) Six days: deduct 80%;
(g) Seven days: A result of zero is awarded for the assessment task.

The following penalties will apply for a late submission for an online examination:
Less than 15 minutes: No penalty
From 15 minutes to 30 minutes: 20% penalty
More than 30 minutes: 100% penalty

Links to relevant University policy and procedures

For more information on Academic Learning & Teaching categories including:

  • Assessment: Courses and Coursework Programs
  • Review of Assessment and Final Grades
  • Supplementary Assessment
  • Central Examinations
  • Deferred Examinations
  • Student Conduct
  • Students with a Disability

For more information, visit https://www.usc.edu.au/explore/policies-and-procedures#academic-learning-and-teaching

Student Charter

UniSC is committed to excellence in teaching, research and engagement in an environment that is inclusive, inspiring, safe and respectful. The Student Charter sets out what students can expect from the University, and what in turn is expected of students, to achieve these outcomes.

General Enquiries

  • In person:
    • UniSC Sunshine Coast - Student Central, Ground Floor, Building C, 90 Sippy Downs Drive, Sippy Downs
    • UniSC Moreton Bay - Service Centre, Ground Floor, Foundation Building, Gympie Road, Petrie
    • UniSC SouthBank - Student Central, Building A4 (SW1), 52 Merivale Street, South Brisbane
    • UniSC Gympie - Student Central, 71 Cartwright Road, Gympie
    • UniSC Fraser Coast - Student Central, Student Central, Building A, 161 Old Maryborough Rd, Hervey Bay
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  • Tel:+61 7 5430 2890
  • Email:studentcentral@usc.edu.au